The 2020s will go down in history as one of the best times to be a stock market investor. After a brief dip in 2022, the benchmark S&P 500 (SNPINDEX: ^GSPC) has soared by an average of roughly 21% per year — double its 100-year average annual return of 10%.
That said, history shows us that significant drawdowns often follow periods of unusually elevated stock market gains as the market reverts to its long-running mean. Let’s discuss some of the challenges facing this bull market to try to figure out what might come next.
Missed AI’s “Act 1”? Act 2 Could Be 14x Bigger. Most investors think they missed the AI boat because they didn’t buy Nvidia in 2005. But according to our analysts, we’re only at the end of “Act 1″—the R&D phase. “Act 2” is the global rollout. Continue »
The problem with valuations
By now, most investors have probably heard about the cyclically adjusted price-to-earnings (CAPE) ratio. This metric compares the price of the S&P 500 with its average inflation-adjusted earnings over the past decade. The long time frame smooths out short-term fluctuations, providing a clearer picture of the market’s value relative to historical norms.
Right now, the CAPE ratio stands at 40.5, which is well above its average of 17.4. The other two major peaks occurred before the Great Depression in 1929 and during the dot-com bubble in 1999, when it hit its all-time high of 44.19. Both milestones were followed by substantial declines in equity prices over the subsequent years as the speculative bubbles deflated.
History might not repeat itself, but it certainly tends to rhyme. And the current generative AI bubble shows strong similarities with the dot-com bubble two decades ago. Both are being driven by a transformational new technology that may not pay off as quickly as its major backers expect.
Can the generative AI bubble burst?
On some level, the generative AI boom looks much safer than the dot-com bubble because it is being driven by stable, profitable companies instead of speculative start-ups. Valuations are also quite reasonable, with major players like Nvidia and Micron Technology boasting forward price-to-earnings (P/E) multiples of just 25 and 6, respectively. Their revenue is simply growing so fast that their stock prices can’t keep up.
But while these factors will probably limit the scale of a potential crash, they don’t make one impossible, because the revenue itself comes from what appears to be an increasingly unsustainable source.
Goldman Sachs estimates that hyperscalers (Nvidia and Micron’s clients) could spend $800 billion on AI-related capital expenditures (capex) in 2026 alone. And if current trends continue, spending could rise to $1.4 trillion by 2028.
